A joint population pharmacokinetic model to assess the high variability of whole-blood and intracellular tacrolimus in early adult renal transplant recipients.

Tacrolimus (TAC) has high pharmacokinetic (PK) variability during the early transplantation period. The relationships between whole-blood and intracellular TAC concentrations and clinical outcomes remain controversial. This study identifies the factors affecting the PK variability of TAC and characterizes the relationships between whole-blood and intracellular TAC concentrations. Data regarding whole-blood TAC concentrations of 1,787 samples from 215 renal transplant recipients (<90 days postoperative) across two centers and intracellular TAC concentrations (648 samples) digitized from previous studies were analyzed using nonlinear mixed-effects modeling. The effects of potential covariates were screened, and the distribution of whole-blood to intracellular TAC concentration ratios (RWB:IC) was estimated. The final model was evaluated using bootstrap, goodness of fit, and prediction-corrected visual predictive checks. The optimal dosing regimens and target ranges for each type of immune cell subsets were determined using Monte Carlo simulations. A two-compartment model adequately described the data, and the estimated mean TAC CL/F was 23.6 L·h-1 (relative standard error: 11.5 %). The hematocrit level, CYP3A5*3 carrier status, co-administration with Wuzhi capsules, and tapering prednisolone dose may contribute to the high variability of TAC PK variability during the early post-transplant period. The estimated RWB:IC of all TAC concentrations in peripheral blood mononuclear cells (PBMCs) was 4940, and inter-center variability of PBMCs was observed. The simulated TAC target range in PBMCs was 20.2-85.9 pg·million cells-1. Inter-center variability in intracellular concentrations should be taken into account in further analyses. TAC dosage adjustments can be guided based on PK/PD variability and simulated intracellular concentrations.

Computing the Relative Affinity of Chlorophylls a and b to Light-Harvesting Complex II.

In plants and algae, the primary antenna protein bound to photosystem II is light-harvesting complex II (LHCII), a pigment-protein complex that binds eight chlorophyll (Chl) a molecules and six Chl b molecules. Chl a and Chl b differ only in that Chl a has a methyl group (-CH3) on one of its pyrrole rings, while Chl b has a formyl group (-CHO) at that position. This blue-shifts the Chl b absorbance relative to Chl a. It is not known how the protein selectively binds the right Chl type at each site. Knowing the selection criteria would allow the design of light-harvesting complexes that bind different Chl types, modifying an organism to utilize the light of different wavelengths. The difference in the binding affinity of Chl a and Chl b in pea and spinach LHCII was calculated using multiconformation continuum electrostatics and free energy perturbation. Both methods have identified some Chl sites where the bound Chl type (a or b) has a significantly higher affinity, especially when the protein provides a hydrogen bond for the Chl b formyl group. However, the Chl a sites often have little calculated preference for one Chl type, so they are predicted to bind a mixture of Chl a and b. The electron density of the spinach LHCII was reanalyzed, which, however, confirmed that there is negligible Chl b in the Chl a-binding sites. It is suggested that the protein chooses the correct Chl type during folding, segregating the preferred Chl to the correct binding site.

Modeling the protein binding non-linearity in population pharmacokinetic model of valproic acid in children with epilepsy: a systematic evaluation study.

Background: Several studies have investigated the population pharmacokinetics (popPK) of valproic acid (VPA) in children with epilepsy. However, the predictive performance of these models in the extrapolation to other clinical environments has not been studied. Hence, this study evaluated the predictive abilities of pediatric popPK models of VPA and identified the potential effects of protein binding modeling strategies. Methods: A dataset of 255 trough concentrations in 202 children with epilepsy was analyzed to assess the predictive performance of qualified models, following literature review. The evaluation of external predictive ability was conducted by prediction- and simulation-based diagnostics as well as Bayesian forecasting. Furthermore, five popPK models with different protein binding modeling strategies were developed to investigate the discrepancy among the one-binding site model, Langmuir equation, dose-dependent maximum effect model, linear non-saturable binding equation and the simple exponent model on model predictive ability. Results: Ten popPK models were identified in the literature. Co-medication, body weight, daily dose, and age were the four most commonly involved covariates influencing VPA clearance. The model proposed by Serrano et al. showed the best performance with a median prediction error (MDPE) of 1.40%, median absolute prediction error (MAPE) of 17.38%, and percentages of PE within 20% (F20, 55.69%) and 30% (F30, 76.47%). However, all models performed inadequately in terms of the simulation-based normalized prediction distribution error, indicating unsatisfactory normality. Bayesian forecasting enhanced predictive performance, as prior observations were available. More prior observations are needed for model predictability to reach a stable state. The linear non-saturable binding equation had a higher predictive value than other protein binding models. Conclusion: The predictive abilities of most popPK models of VPA in children with epilepsy were unsatisfactory. The linear non-saturable binding equation is more suitable for modeling non-linearity. Moreover, Bayesian forecasting with prior observations improved model fitness.

A tandem effect of atomically isolated copper-nitrogen sites and copper clusters enhances CO2 electroreduction to ethylene.

Direct electrochemical conversion of CO2 to C2H4 with high selectivity is highly desirable for lowering CO2 emissions. However, limited by the slow *CO dimerization step at a single active site, it is difficult for current electrocatalysts to further improve the selectivity toward C2H4. Here we report a tandem catalyst PDI-Cu/Cu with Cu-N sites and Cu clusters, synthesized by uniformly dispersing Cu clusters on a coordination polymer PDI-Cu, which has atomically isolated Cu-N sites. This tandem catalyst, which has an optimal content of Cu clusters, shows more than 2 times the enhancement in C2H4 production compared with that of the non-tandem catalyst PDI/Cu. Density functional theory (DFT) calculations support the tandem reaction mechanism, where Cu-N sites first reduce CO2 into highly concentrated CO and then the CO migrates to the surfaces of Cu clusters for further conversion into C2H4, decoupling the complex C2H4 generation pathway at single active sites into a two-step tandem reaction. This work offers a rational approach to design electrocatalysts for further boosting the selectivity of the CO2RR to C2+ products via a tandem route.

Tools for analyzing protonation states and for tracing proton transfer pathways with examples from the Rb. sphaeroides photosynthetic reaction centers.

Protons participate in many reactions. In proteins, protons need paths to move in and out of buried active sites. The vectorial movement of protons coupled to electron transfer reactions establishes the transmembrane electrochemical gradient used for many reactions, including ATP synthesis. Protons move through hydrogen bonded chains of waters and hydroxy side chains via the Grotthuss mechanism and by proton binding and release from acidic and basic residues. MCCE analysis shows that proteins exist in a large number of protonation states. Knowledge of the equilibrium ensemble can provide a rational basis for setting protonation states in simulations that fix them, such as molecular dynamics (MD). The proton path into the QB site in the bacterial reaction centers (RCs) of Rb. sphaeroides is analyzed by MD to provide an example of the benefits of using protonation states found by the MCCE program. A tangled web of side chains and waters link the cytoplasm to QB. MCCE analysis of snapshots from multiple trajectories shows that changing the input protonation state of a residue in MD biases the trajectory shifting the proton affinity of that residue. However, the proton affinity of some residues is more sensitive to the input structure. The proton transfer networks derived from different trajectories are quite robust. There are some changes in connectivity that are largely restricted to the specific residues whose protonation state is changed. Trajectories with QB•- are compared with earlier results obtained with QB [Wei et. al Photosynthesis Research volume 152, pages153-165 (2022)] showing only modest changes. While introducing new methods the study highlights the difficulty of establishing the connections between protein conformation.

Applying machine learning to the pharmacokinetic modeling of cyclosporine in adult renal transplant recipients: a multi-method comparison.

Objective: The aim of this study was to identify the important factors affecting cyclosporine (CsA) blood concentration and estimate CsA concentration using seven different machine learning (ML) algorithms. We also assessed the predictability of established ML models and previously built population pharmacokinetic (popPK) model. Finally, the most suitable ML model and popPK model to guide precision dosing were determined. Methods: In total, 3,407 whole-blood trough and peak concentrations of CsA were obtained from 183 patients who underwent initial renal transplantation. These samples were divided into model-building and evaluation sets. The model-building set was analyzed using seven different ML algorithms. The effects of potential covariates were evaluated using the least absolute shrinkage and selection operator algorithms. A separate evaluation set was used to assess the ability of all models to predict CsA blood concentration. R squared (R 2) scores, median prediction error (MDPE), median absolute prediction error (MAPE), and the percentages of PE within 20% (F20) and 30% (F30) were calculated to assess the predictive performance of these models. In addition, previously built popPK model was included for comparison. Results: Sixteen variables were selected as important covariates. Among ML models, the predictive performance of nonlinear-based ML models was superior to that of linear regression (MDPE: 3.27%, MAPE: 34.21%, F20: 30.63%, F30: 45.03%, R 2 score: 0.68). The ML model built with the artificial neural network algorithm was considered the most suitable (MDPE: -0.039%, MAPE: 25.60%, F20: 39.35%, F30: 56.46%, R 2 score: 0.75). Its performance was superior to that of the previously built popPK model (MDPE: 5.26%, MAPE: 29.22%, F20: 33.94%, F30: 51.22%, R 2 score: 0.68). Furthermore, the application of the most suitable model and the popPK model in clinic showed that most dose regimen recommendations were reasonable. Conclusion: The performance of these ML models indicate that a nonlinear relationship for covariates may help to improve model predictability. These results might facilitate the application of ML models in clinic, especially for patients with unstable status or during initial dose optimization.

Effects of Atractylon on Proliferation and Apoptosis of Intestinal Cancer Cells Through PI3K/AKT/mTOR Signaling Pathway.

The study aimed to explore the effects of atractylon on the proliferation and apoptosis of intestinal cancer cells through the phosphatidylinositol 3-hydroxy kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling pathway. The intestinal cancer HT29 cell lines were cultured in vitro, and atractylon at different concentrations (15 and 30 mg/mL) was added. Then cell proliferative activity was detected via cell counting kit-8 (CCK8) assay, and the proportion of positive cells was determined using EdU staining. The content of interferon-γ (INF-γ), tumor necrosis factor-α (TNF-α) and matrix metalloproteinase-9 (MMP-9) was detected via enzyme-linked immunosorbent assay (ELISA), and the apoptosis of HT29 cells was detected through terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) staining. Moreover, reverse transcription-polymerase chain reaction (RT-PCR) was performed to determine the messenger ribonucleic acid (mRNA) levels of proliferation, apoptosis and PI3K/AKT/mTOR signaling pathway-related genes, and Western blotting was used to analyze the expression of the PI3K/AKT/mTOR signaling pathway. The cell growth status was poorer with a lower density in the 15 mg/mL atractylon group and basically normal morphological structure in the 30 mg/mL atractylon group. The number of cells significantly declined and the proliferative activity was also significantly weakened in the 30 mg/mL atractylon group. There were obviously more apoptotic cells in the 30 mg/mL atractylon group. Besides, INF-γ, TNF-α and MMP-9 were all evidently decreased in the 30 mg/mL atractylon group. Expressions of B-cell lymphoma-2 (Bcl-2), PI3K, AKT and mTOR obviously declined in the 30 mg/mL atractylon group, and they were raised in the NC group, while the expression of Caspase3 showed the opposite trends. Atractylon at an appropriate concentration can inhibit the proliferation and promote the apoptosis of intestinal cancer cells by suppressing the PI3K/AKT/mTOR signaling pathway, which can be used to treat colorectal cancer and other related diseases.

Evaluation and Application of Population Pharmacokinetic Models for Identifying Delayed Methotrexate Elimination in Patients With Primary Central Nervous System Lymphoma.

Objective: Several population pharmacokinetic (popPK) models have been developed to determine the sources of methotrexate (MTX) PK variability. It remains unknown if these published models are precise enough for use or if a new model needs to be built. The aims of this study were to 1) assess the predictability of published models and 2) analyze the potential risk factors for delayed MTX elimination. Methods: A total of 1458 MTX plasma concentrations, including 377 courses (1-17 per patient), were collected from 77 patients who were receiving high-dose MTX for the treatment of primary central nervous system lymphoma in Huashan Hospital. PopPK analysis was performed using the NONMEM® software package. Previously published popPK models were selected and rebuilt. A new popPK model was then constructed to screen potential covariates using a stepwise approach. The covariates were included based on the combination of theoretical mechanisms and data properties. Goodness-of-fit plots, bootstrap, and prediction- and simulation-based diagnostics were used to determine the stability and predictive performance of both the published and newly built models. Monte Carlo simulations were conducted to qualify the influence of risk factors on the incidence of delayed elimination. Results: Among the eight evaluated published models, none presented acceptable values of bias or inaccuracy. A two-compartment model was employed in the newly built model to describe the PK of MTX. The estimated mean clearance (CL/F) was 4.91 L h-1 (relative standard error: 3.7%). Creatinine clearance, albumin, and age were identified as covariates of MTX CL/F. The median and median absolute prediction errors of the final model were -10.2 and 36.4%, respectively. Results of goodness-of-fit plots, bootstrap, and prediction-corrected visual predictive checks indicated the high predictability of the final model. Conclusions: Current published models are not sufficiently reliable for cross-center use. The elderly patients and those with renal dysfunction, hypoalbuminemia are at higher risk of delayed elimination.

Comparison of proton transfer paths to the QA and QB sites of the Rb. sphaeroides photosynthetic reaction centers.

The photosynthetic bacterial reaction centers from purple non-sulfur bacteria use light energy to drive the transfer of electrons from cytochrome c to ubiquinone. Ubiquinone bound in the QA site cycles between quinone, QA, and anionic semiquinone, QA·-, being reduced once and never binding protons. In the QB site, ubiquinone is reduced twice by QA·-, binds two protons and is released into the membrane as the quinol, QH2. The network of hydrogen bonds formed in a molecular dynamics trajectory was drawn to investigate proton transfer pathways from the cytoplasm to each quinone binding site. QA is isolated with no path for protons to enter from the surface. In contrast, there is a complex and tangled network requiring residues and waters that can bring protons to QB. There are three entries from clusters of surface residues centered around HisH126, GluH224, and HisH68. The network is in good agreement with earlier studies, Mutation of key nodes in the network, such as SerL223, were previously shown to slow proton delivery. Mutational studies had also shown that double mutations of residues such as AspM17 and AspL210 along multiple paths in the network presented here slow the reaction, while single mutations do not. Likewise, mutation of both HisH126 and HisH128, which are at the entry to two paths reduce the rate of proton uptake.

Characterizing Protein Protonation Microstates Using Monte Carlo Sampling.

Proteins are polyelectrolytes with acidic and basic amino acids Asp, Glu, Arg, Lys, and His, making up ≈25% of the residues. The protonation state of residues, cofactors, and ligands defines a "protonation microstate". In an ensemble of proteins some residues will be ionized and others neutral, leading to a mixture of protonation microstates rather than in a single one as is often assumed. The microstate distribution changes with pH. The protein environment also modifies residue proton affinity so microstate distributions change in different reaction intermediates or as ligands are bound. Particular protonation microstates may be required for function, while others exist simply because there are many states with similar energy. Here, the protonation microstates generated in Monte Carlo sampling in MCCE are characterized in HEW lysozyme as a function of pH and bacterial photosynthetic reaction centers (RCs) in different reaction intermediates. The lowest energy and highest probability microstates are compared. The ΔG, ΔH, and ΔS between the four protonation states of Glu35 and Asp52 in lysozyme are shown to be calculated with reasonable precision. At pH 7 the lysozyme charge ranges from 6 to 10, with 24 accepted protonation microstates, while RCs have ≈50,000. A weighted Pearson correlation analysis shows coupling between residue protonation states in RCs and how they change when the quinone in the QB site is reduced. Protonation microstates can be used to define input MD parameters and provide insight into the motion of protons coupled to reactions.

Factors Affecting Time-Varying Clearance of Cyclosporine in Adult Renal Transplant Recipients: A Population Pharmacokinetic Perspective.

AIM: The pharmacokinetic (PK) properties of cyclosporine (CsA) in renal transplant recipients are patient- and time-dependent. Knowledge of this time-related variability is necessary to maintain or achieve CsA target exposure. Here, we aimed to identify factors explaining variabilities in CsA PK properties and characterize time-varying clearance (CL/F) by performing a comprehensive analysis of CsA PK factors using population PK (popPK) modeling of long-term follow-up data from our institution. METHODS: In total, 3674 whole-blood CsA concentrations from 183 patients who underwent initial renal transplantation were analyzed using nonlinear mixed-effects modeling. The effects of potential covariates were selected according to a previous study and well-accepted theoretical mechanisms. Model-informed individualized therapeutic regimens were also evaluated. RESULTS: A two-compartment model adequately described the data and the estimated mean CsA CL/F was 32.6 L h-1 (relative standard error: 5%). Allometrically scaled body size, hematocrit (HCT) level, CGC haplotype carrier status, and postoperative time may contribute to CsA PK variability. The CsA bioavailability in patients receiving a prednisolone dose (PD) of 80 mg was 20.6% lower than that in patients receiving 20 mg. A significant decrease (52.6%) in CL/F was observed as the HCT increased from 10.5% to 60.5%. The CL/F of the non-CGC haplotype carrier was 14.4% lower than that of the CGC haplotype carrier at 3 months post operation. CONCLUSIONS: By monitoring body size, HCT, PD, and CGC haplotype, changes in CsA CL/F over time could be predicted. Such information could be used to optimize CsA therapy. CsA dose adjustments should be considered in different postoperative periods.

Poor Person's pH Simulation of Membrane Proteins.

pH conditions are central to the functioning of all biomolecules. However, implications of pH changes are nontrivial on a molecular scale. Though a rigorous microscopic definition of pH exists, its implementation in classical molecular dynamics (MD) simulations is cumbersome, and more so in large integral membrane systems. In this chapter, an integrative pipeline is described that combines Multi-Conformation Continuum Electrostatics (MCCE) computations with MD simulations to capture the effect of transient protonation states on the coupled conformational changes in transmembrane proteins. The core methodologies are explained, and all the software required to set up this pipeline are outlined with their key parameters. All associated analyses of structure and function are provided using two case studies, namely those of bioenergetic complexes: NADH dehydrogenase (complex I) and Vo domain of V-type ATPase. The hybrid MCCE-MD pipeline has allowed the discovery of hydrogen bond networks, ligand binding pathways, and disease-causing mutations.

Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge.

The Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pKa for 22 compounds. The dataset was composed of a series of N-acylsulfonamides and related bioisosteres. 17 research groups participated in the log P challenge, submitting 33 blind submissions total. For the pKa challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water log P predictions in the SAMPL7 challenge was lower than octanol-water log P predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pKa challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pKa values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pKa prediction methods.

Effects of SCN1A and SCN2A polymorphisms on responsiveness to valproic acid monotherapy in epileptic children.

PURPOSE: This study was conducted to explore the influence of genetic variations on responsiveness to valproic acid (VPA) monotherapy among Chinese children with epilepsy. METHODS: One hundred and forty epileptic children taking VPA as monotherapy were enrolled, and at least one-year follow-up was obtained to assess the therapeutic outcome. Twenty-seven single nucleotide polymorphisms (SNPs) within twelve candidate genes correlated with the metabolic enzymes, transporters and targets of VPA were genotyped. The effects of selected polymorphisms on VPA efficacy were identified by binary logistic regression analysis adjusting for potential confounders. RESULTS: SCN2A rs2304016 (A > G) AG genotype was more common among the VPA-resistant patients in comparison with the VPA-responsive patients (OR = 3.18, 95 % CI = 1.10-9.14, P = 0.032), and in subgroup of focal seizure, lower frequency of VPA resistance was found in epileptic children with SCN1A rs2298771 AG genotype than those with AA genotype (OR = 0.11, 95 % CI = 0.01-0.91, P = 0.040). CONCLUSIONS: Our study indicated that SCN2A rs2304016 and SCN1A rs2298771 polymorphisms might be associated with the response to VPA monotherapy in Chinese epileptic children. Further and larger researches are required to validate these results.

Incorporating nonlinear kinetics to improve predictive performance of population pharmacokinetic models for ciclosporin in adult renal transplant recipients: A comparison of modelling strategies.

BACKGROUND: Ciclosporin has been shown to follow nonlinear pharmacokinetics (PK) in renal transplant recipients who received ciclosporin (NeoralⓇ, Novartis)-based triple immunosuppressive therapy. Some of these nonlinear properties have not been fully considered in population PK (popPK) analysis. Therefore, the aim of this study was to determine the potential influence of nonlinearity and the functional forms of covariates on model predictability as well as to analyse multiple nonlinear factors in the in vivo process. METHODS: A total of 2969 ciclosporin whole-blood measurements, including 1328 pre-dose and 1641 2-h post-dose concentrations, were collected from 173 patients who underwent their first renal transplantation. Four popPK models based on different modelling strategies were developed to investigate the discrepancy between empirical and theory-based, linear and nonlinear compartmental kinetic models and empirical formulae on model predictability. Prediction and simulation-based diagnostics (prediction-corrected visual predictive checks) were performed to determine the stability and predictive performance of these four models. RESULTS: Model predictability improved when nonlinearity was considered. The theory-based nonlinear model which incorporated nonlinear property based on known theoretical relationships performed better than the other two compartmental models. The nonlinear Michaelis-Menten model showed a remarkable improvement in predictive performance compared to the other three compartmental models. The saturated binding of ciclosporin to erythrocytes, auto-inhibition induced by the inhibitory effects of ciclosporin on cytochrome P450 3A4/P-glycoprotein may have contributed to the nonlinearity. Ciclosporin-prednisolone drug interaction should be given serious consideration in clinical settings. CONCLUSIONS: Incorporation of nonlinear properties is likely to be a promising approach for improving ciclosporin model predictability. Theory-based modelling is helpful to improve model predictability. However, ciclosporin nonlinear kinetics resources need further investigation.

Hydrogen bond network analysis reveals the pathway for the proton transfer in the E-channel of T. thermophilus Complex I.

Complex I, NADH-ubiquinone oxidoreductase, is the first enzyme in the mitochondrial and bacterial aerobic respiratory chain. It pumps four protons through four transiently open pathways from the high pH, negative, N-side of the membrane to the positive, P-side driven by the exergonic transfer of electrons from NADH to a quinone. Three protons transfer through subunits descended from antiporters, while the fourth, E-channel is unique. The path through the E-channel is determined by a network analysis of hydrogen bonded pathways obtained by Monte Carlo sampling of protonation states, polar hydrogen orientation and water occupancy. Input coordinates are derived from molecular dynamics trajectories comparing oxidized, reduced (dihydro) and no menaquinone-8 (MQ). A complex proton transfer path from the N- to the P-side is found consisting of six clusters of highly connected hydrogen-bonded residues. The network connectivity depends on the presence of quinone and its redox state, supporting a role for this cofactor in coupling electron and proton transfers. The N-side is more organized with MQ-bound complex I facilitating proton entry, while the P-side is more connected in the apo-protein, facilitating proton exit. Subunit Nqo8 forms the core of the E channel; Nqo4 provides the N-side entry, Nqo7 and then Nqo10 join the pathway in the middle, while Nqo11 contributes to the P-side exit.

Identifying the proton loading site cluster in the ba3 cytochrome c oxidase that loads and traps protons.

Cytochrome c Oxidase (CcO) is the terminal electron acceptor in aerobic respiratory chain, reducing O2 to water. The released free energy is stored by pumping protons through the protein, maintaining the transmembrane electrochemical gradient. Protons are held transiently in a proton loading site (PLS) that binds and releases protons driven by the electron transfer reaction cycle. Multi-Conformation Continuum Electrostatics (MCCE) was applied to crystal structures and Molecular Dynamics snapshots of the B-type Thermus thermophilus CcO. Six residues are identified as the PLS, binding and releasing protons as the charges on heme b and the binuclear center are changed: the heme a3 propionic acids, Asp287, Asp372, His376 and Glu126B. The unloaded state has one proton and the loaded state two protons on these six residues. Different input structures, modifying the PLS conformation, show different proton distributions and result in different proton pumping behaviors. One loaded and one unloaded protonation states have the loaded/unloaded states close in energy so the PLS binds and releases a proton through the reaction cycle. The alternative proton distributions have state energies too far apart to be shifted by the electron transfers so are locked in loaded or unloaded states. Here the protein can use active states to load and unload protons, but has nearby trapped states, which stabilize PLS protonation state, providing new ideas about the CcO proton pumping mechanism. The distance between the PLS residues Asp287 and His376 correlates with the energy difference between loaded and unloaded states.

Relative stability of the S2 isomers of the oxygen evolving complex of photosystem II.

The oxidation of water to O2 is catalyzed by the Oxygen Evolving Complex (OEC), a Mn4CaO5 complex in Photosystem II (PSII). The OEC is sequentially oxidized from state S0 to S4. The S2 state, (MnIII)(MnIV)3, coexists in two redox isomers: S2,g=2, where Mn4 is MnIV and S2,g=4.1, where Mn1 is MnIV. Mn4 has two terminal water ligands, whose proton affinity is affected by the Mn oxidation state. The relative energy of the two S2 redox isomers and the protonation state of the terminal water ligands are analyzed using classical multi-conformer continuum electrostatics (MCCE). The Monte Carlo simulations are done on QM/MM optimized S1 and S2 structures docked back into the complete PSII, keeping the protonation state of the protein at equilibrium with the OEC redox and protonation states. Wild-type PSII, chloride-depleted PSII, PSII in the presence of oxidized YZ/protonated D1-H190, and the PSII mutants D2-K317A, D1-D61A, and D1-S169A are studied at pH 6. The wild-type PSII at pH 8 is also described. In qualitative agreement with experiment, in wild-type PSII, the S2,g=2 redox isomer is the lower energy state; while chloride depletion or pH 8 stabilizes the S2,g=4.1 state and the mutants D2-K317A, D1-D61A, and D1-S169A favor the S2,g=2 state. The protonation states of D1-E329, D1-E65, D1-H337, D1-D61, and the terminal waters on Mn4 (W1 and W2) are affected by the OEC oxidation state. The terminal W2 on Mn4 is a mixture of water and hydroxyl in the S2,g=2 state, indicating the two water protonation states have similar energy, while it remains neutral in the S1 and S2,g=4.1 states. In wild-type PSII, advancement to S2 leads to negligible proton loss and so there is an accumulation of positive charge. In the analyzed mutations and Cl- depleted PSII, additional deprotonation is found upon formation of S2 state.

Rosmarinic acid inhibits nicotine-induced C-reactive protein generation by inhibiting NLRP3 inflammasome activation in smooth muscle cells.

Atherosclerosis is widely known to be a chronic inflammatory disease. C-reactive protein (CRP), an important inflammatory factor, plays an essential role in the pathogenesis of atherosclerosis. Nicotine, the main addictive component of cigarette, has been shown to induce the production of CRP. The aim of this study was to investigate the effect of rosmarinic acid (RA), a polyphenol with antiinflammatory activity, on nicotine-induced elevation of CRP in vascular smooth muscle cells (VSMCs). We found that pretreatment of VSMCs with RA attenuated nicotine-induced expression of CRP in a time- and dose-dependant manner. In addition, RA also inhibited the activation of NLR family pyrin domain containing 3 (NLRP3) inflammasome and reactive oxygen species (ROS) production resulting from nicotine treatment in VSMCs. To confirm these findings in vivo, we constructed a nicotine-induced atherosclerosis rat model. RA did not significantly reduce the serum nicotine level of the rats, whereas it significantly decreased the levels of serum lipids, including concentrations of cholesterol, triglycerides, and low-density lipoprotein cholesterol, and the serum level of CRP. RA also led to diminished nicotine-induced activation of NLRP3 inflammasome and elevation in the CRP level in the aortic tissue of the model rats. The results of this study suggested a protective role of RA in nicotine-induced atherosclerosis by inhibiting the ROS-NLRP3 inflammasome-CRP axial, and RA therefore represented a potential effective therapeutic approach to atherosclerosis, in particular for those who smoke.

External evaluation of population pharmacokinetic models for ciclosporin in adult renal transplant recipients.

AIMS: Several population pharmacokinetic (popPK) models for ciclosporin (CsA) in adult renal transplant recipients have been constructed to optimize the therapeutic regimen of CsA. However, little is known about their predictabilities when extrapolated to different clinical centres. Therefore, this study aimed to externally evaluate the predictive ability of CsA popPK models and determine the potential influencing factors. METHODS: A literature search was conducted and the predictive performance was determined for each selected model using an independent data set of 62 patients (471 predose and 500 2-h postdose concentrations) from our hospital. Prediction-based diagnostics and simulation-based normalized prediction distribution error were used to evaluate model predictability. The influence of prior information was assessed using Bayesian forecasting. Additionally, potential factors influencing model predictability were investigated. RESULTS: Seventeen models extracted from 17 published popPK studies were assessed. Prediction-based diagnostics showed that ethnicity potentially influenced model transferability. Simulation-based normalized prediction distribution error analyses indicated misspecification in most of the models, especially regarding variance. Bayesian forecasting demonstrated that the predictive performance of the models substantially improved with 2-3 prior observations. The predictability of nonlinear Michaelis-Menten models was superior to that of linear compartmental models when evaluating the impact of structural models, indicating the underlying nonlinear kinetics of CsA. Structural model, ethnicity, covariates and prior observations potentially affected model predictability. CONCLUSIONS: Structural model is the predominant factor influencing model predictability. Incorporation of nonlinear kinetics in CsA popPK modelling should be considered. Moreover, Bayesian forecasting substantially improved model predictability.